Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

Bayesian statistics have made great strides in recent years, developing a class of methods for estimation and inference via stochastic simulation known as Markov Chain Monte Carlo (MCMC) methods. MCMC ...

In this article, we systematically introduce the just another Gibbs sampler (JAGS) software program to fit common Bayesian cognitive diagnosis models (CDMs) including the deterministic inputs, noisy ...

Alice and Bob are playing a game in which the first person to get 6 points wins. The way each point is decided is a little strange. The Casino has a pool table that Alice and Bob can't see. Before the ...

Carlin and Louis - Bayes and Empirical Bayes Methods for Data Analysis Gelman, Carlin, Stern and Rubin - Bayesian Data Analysis Bernardo and Smith - Bayesian Theory Gilks, Richardson and Spiegelhalter ...

David Vaux argues that experimental biologists should be better versed in classical statistics (Nature 492, 180–181; 2012). We suggest that they might also join the shift to Bayesian statistics that ...